When Visual Disparity Generation Meets Semantic Segmentation: A Mutual Encouragement Approach

Semantic segmentation and depth estimation play important roles in the field of autonomous driving. In recent years, the advantages of Convolutional Neural Networks (CNNs) have allowed these two topics to flourish. However, people always solve these two tasks separately and rarely solve them in a united model. In this paper, we propose a Mutual Encouragement Network (MENet), which includes a semantic segmentation branch and a disparity regression branch, and simultaneously generates semantic map and visual disparity. In the cost volume construction phase, the depth information is embedded in the semantic segmentation branch to increase contextual understanding. Similarly, the semantic information is also included in the disparity regression branch to generate more accurate disparity. Two branches mutually promote each other during training phase and inference phase. We conducted our method on the popular dataset KITTI, and the experimental results show that our method can outperform the state-of-the-art methods on both visual disparity generation and semantic segmentation. In addition, extensive ablation studies also demonstrate that the two tasks in our method can facilitate each other significantly with the proposed approach.

Automated summary

In this paper, we propose a Mutual Encouragement Network (MENet) that simultaneously achieves semantic segmentation and depth estimation, and outperforms existing methods in experiments.